Units / MAT1830

MAT1830 · Discrete mathematics for computer science

Official Handbook

2026 Handbook6 credit pointsLevel 1Faculty of Information Technology

Last checked: 22 Aug 2026 UTC

Overview

This unit introduces fundamental discrete mathematics topics including combinatorics, sets, relations and functions; methods of logic and proof, especially proof by induction; probability theory, Bayes' theorem; recursion; recurrence relations; trees and other graphs. It establishes the mathematical basis required for studies in Computer Science and Software Engineering.

Areas of study: Applied mathematics Computational science Mathematics Software engineering

Offerings

CampusTeaching periodMode
MalaysiaSecond semesterTeaching activities are on-campus (ON-CAMPUS)
ClaytonFirst semesterSome activities have a choice of on-campus or online teaching activities (FLEXIBLE)
MalaysiaFirst semesterTeaching activities are on-campus (ON-CAMPUS)

Assessment

The Handbook lists an examination for this unit.

#AssessmentTypeWeightHurdle
1Weekly quizzesQuiz / Test35%
2AssignmentsExercise15%
3Scheduled final exam (3 hours and 10 minutes)Examination50%

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

prohibitions

  • ITI9004 — Mathematical foundations for data science and AI
  • FIT1058 — Foundations of computing

Joined by AND.

Learning outcomes

  1. Identify basic methods of proof, particularly induction, and apply them to solve problems in mathematics and computer science;
  2. Manipulate sets, relations, functions and their associated concepts, and apply these to solve problems in mathematics and computer science;
  3. Use and analyse simple first and second order recurrence relations;
  4. Use trees and graphs to solve problems in computer science;
  5. Apply counting principles in combinatorics;
  6. Describe the principles of elementary probability theory, evaluate conditional probabilities and use Bayes' Theorem.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. Applied sessions start from Week 2 of the semester.

ActivityDuration
Applied sessions22 hours
Seminars36 hours

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